How to incorporate head movements in MEG analysis

Description

Changes in head position during MEG sessions may cause a significant error in the source localization. Besides, the mixture of different head positions over time adds variance to the data that is not accounted for by the experimental manipulation. Thus head movements may deteriorate statistical sensitivity when analyzing MEG on both sensor and source levels. It is therefore recommended to incorporate head movements in the offline MEG analysis, see Stolk et al., NeuroImage 2013.

Continuous head localization information is stored in HLC channels (Head Localization Channels) in CTF MEG system. An example script here shows how to read these channels in FieldTrip and estimate the amount of movement offline. Information from these channels can also be used to track the head position in real time.
The data used in this example script can be obtained here

In general there are multiple ways that you can use the continuous head localization information.

you can discard a subject or trial(s) from subsequent analysis if he/she moved too much

you can regress out the movements from the processed data

you can compensate the raw data for the movements

you can correct the forward model (i.e. the leadfield) for the spatial blurring that is due to the movements

The first way of dealing with it requires that you visualize and decide on the movements. This is demonstrated in the first half of the example script. A more general description of the data, also containing information on the head movements, is made using ft_qualitycheck. At the donders, this function is run automatically on each new MEG dataset every night. The (visualized) output is stored in /home/common/meg_quality.

The second way of dealing with the movements means that you perform ft_timelockanalysis, ft_freqanalysis or ft_sourceanalysis with the option keeptrials=yes. This will give trial estimates of the ERF, the power or the source strength for each trial. The effect that the variable head position has on those single-trial estimates can be estimated and removed from the data using ft_regressconfound. This method has been found to significantly improve statistical sensivity following head movements, up to 30%, and is therefore demonstrated in the second half of the example script.

The third way of dealing with the movements requires that you make a spatial interpolation of the raw MEG data at each moment in time, in which you correct for the movements. In principle this could be done using the ft_megrealign function, but at this moment (May 2012) that function cannot yet deal with within-session movements.

The fourth way of dealing with the movements is implemented in the ft_headmovement function. It is not explained in further detail on this example page.

The figure illustrates head position changes during 1-hour MEG session (data used for this plot are different from those used in the example above). You may decide to exclude a subject from the subsequent analysis if the head movement exceeds a certain threshold.

Regressing out headposition confounds

MEG experiments typically involve repeated trials of an evoked or induced brain response. A mixture of different head positions over time adds variance to the data that is not accounted for by the experimental manipulation, thus potentially deteriorating statistical sensitivity. By using a general linear model, head movement related trial-by-trial variance can be removed from the data, both at the sensor- and source level. This procedure involves 3 steps:

1) Preprocess the MEG data, for instance pertaining to an ERF analysis at the sensor level. Note the keeptrials = 'yes' when calling ft_timelockanalysis.

2) Create trial-by-trial estimates of head movement. Here one may assume that the head is a rigid body that can be described by 6 parameters (3 translations and 3 rotations). The circumcenter function (see below) gives us these parameters. By demeaning, we obtain the deviations. In other words; translations and rotations relative to the average head position and orientation.

Practical issues

Some features of this GLM-based compensation method need emphasizing. These points are described in more detail in the 'Testing the offline GLM-based head movement compensation' section of Stolk et al., NeuroImage 2013.

First, ft_regressconfound can be applied to timelock, freq, and source data. The estimation of regression coefficients (beta weights of the head position data) is performed separately for each channel and each latency, in the case of timelock data. Consequently, after compensation, the sensor level data cannot be used anymore for source modeling. To employ the GLM based compensation on the source level, single trial estimates for the cortical locations of interest have to be made from the original sensor level data, preferably using a common spatial filter based on all trials. The beta weights are subsequently estimated for each cortical location and the variance in source amplitude over trials that is explained by the head movement is removed. It is therefore recommended to use ft_regressconfound as a final step prior to calling ft_timelockstatistics/ft_freqstatistics/ft_sourcestatistics.

Second, the same trials in the headposition data have to be selected as those present in the MEG data since these two will be fitted. And more or less related; this general linear modeling (GLM) approach only affects the signal variance and not the signal mean over trials (because the constant remains in the data). So when performing a group study, taking the subject mean to the group level statistics will not change these statistics. To benefit from improved statistical sensitivity after using ft_regressconfound, it is advised to take a measure that incorporates the consistency (over trials) of a neural effect to the group level. For instance, the t-descriptive, as obtained using an independent samples t-test on trials of one condition versus that of another. These t-values can then be tested at the group level for rejecting the null-hypothesis of no difference between conditions (T=0).

Finally, note that the circumcenter function is a helper function that calculates the position (geometrical center of the three localizer coils) and orientation of the head. This saves some degrees of freedom (df=6) as compared to taking into account the x,y,z-coordinates of each coil separately (n=3) as regressors (df=9). If you want to also use the squares, cubes, and derivatives as regressors (to account for non-linear effects of head motion on the MEG signal), this can save quite a bit of degrees. However, too large a number of covariates can reduce statistical efficiency for procedures. In that case, Matlab will produce the Warning 'Rank deficient'. A rule of thumb is to roughly have 10% of the sample size (based on chapter 8 of Tabachnick & Fidell (1996)).

Please cite this paper when you have used the offline head movement compensation in your study.